Complex cyber and cyber-physical systems (CPS), including autonomous cars and drones, rely on the seamless integration of computation and physical components.
A CPS might depend on machine learning algorithms for decision making due to the large amounts of data generated during its operation and limited access to models of its environment.
Reinforcement learning (RL) [1]} and optimal control [2]} are two paradigms that have been commonly leveraged to choose actions to maximize an expected reward over the horizon of system operation
when dynamic system behaviors are represented as Markov decision processes (MDP) [3]}.
